Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations400
Missing cells704
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.4 KiB
Average record size in memory208.3 B

Variable types

Numeric14
Categorical7
Boolean5

Alerts

al is highly overall correlated with classification and 8 other fieldsHigh correlation
ane is highly overall correlated with hemo and 1 other fieldsHigh correlation
bgr is highly overall correlated with dm and 1 other fieldsHigh correlation
bu is highly overall correlated with hemo and 2 other fieldsHigh correlation
classification is highly overall correlated with al and 8 other fieldsHigh correlation
dm is highly overall correlated with bgr and 5 other fieldsHigh correlation
hemo is highly overall correlated with al and 11 other fieldsHigh correlation
htn is highly overall correlated with al and 6 other fieldsHigh correlation
id is highly overall correlated with al and 7 other fieldsHigh correlation
pc is highly overall correlated with al and 2 other fieldsHigh correlation
pcc is highly overall correlated with pcHigh correlation
pcv is highly overall correlated with al and 8 other fieldsHigh correlation
rbc is highly overall correlated with al and 2 other fieldsHigh correlation
rc is highly overall correlated with classification and 3 other fieldsHigh correlation
sc is highly overall correlated with al and 4 other fieldsHigh correlation
sg is highly overall correlated with classificationHigh correlation
sod is highly overall correlated with al and 1 other fieldsHigh correlation
su is highly overall correlated with bgr and 1 other fieldsHigh correlation
pcc is highly imbalanced (51.2%) Imbalance
ba is highly imbalanced (69.0%) Imbalance
cad is highly imbalanced (57.9%) Imbalance
age has 9 (2.2%) missing values Missing
bp has 12 (3.0%) missing values Missing
sg has 47 (11.8%) missing values Missing
al has 46 (11.5%) missing values Missing
su has 49 (12.2%) missing values Missing
rbc has 152 (38.0%) missing values Missing
pc has 65 (16.2%) missing values Missing
bgr has 44 (11.0%) missing values Missing
bu has 19 (4.8%) missing values Missing
sc has 17 (4.2%) missing values Missing
sod has 87 (21.8%) missing values Missing
pot has 88 (22.0%) missing values Missing
hemo has 52 (13.0%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
al has 199 (49.8%) zeros Zeros
su has 290 (72.5%) zeros Zeros

Reproduction

Analysis started2025-06-24 06:38:00.293257
Analysis finished2025-06-24 06:38:56.041147
Duration55.75 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.5
Minimum0
Maximum399
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:08:56.331733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.95
Q199.75
median199.5
Q3299.25
95-th percentile379.05
Maximum399
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143
Coefficient of variation (CV)0.57952031
Kurtosis-1.2
Mean199.5
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13366.667
MonotonicityStrictly increasing
2025-06-24T12:08:56.677293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
263 1
 
0.2%
273 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
268 1
 
0.2%
267 1
 
0.2%
266 1
 
0.2%
Other values (390) 390
97.5%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
399 1
0.2%
398 1
0.2%
397 1
0.2%
396 1
0.2%
395 1
0.2%
394 1
0.2%
393 1
0.2%
392 1
0.2%
391 1
0.2%
390 1
0.2%

age
Real number (ℝ)

Missing 

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.483376
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:08:57.028360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.169714
Coefficient of variation (CV)0.33350016
Kurtosis0.057840495
Mean51.483376
Median Absolute Deviation (MAD)10
Skewness-0.66825947
Sum20130
Variance294.79908
MonotonicityNot monotonic
2025-06-24T12:08:57.337183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
4.8%
65 17
 
4.2%
48 12
 
3.0%
50 12
 
3.0%
55 12
 
3.0%
47 11
 
2.8%
56 10
 
2.5%
59 10
 
2.5%
45 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

bp
Real number (ℝ)

Missing 

Distinct11
Distinct (%)2.8%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.082474
Minimum0
Maximum180
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:08:57.588474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range180
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.736739
Coefficient of variation (CV)0.19369427
Kurtosis9.520002
Mean76.082474
Median Absolute Deviation (MAD)10
Skewness0.63877298
Sum29520
Variance217.17147
MonotonicityNot monotonic
2025-06-24T12:08:57.813287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
80 115
28.7%
70 111
27.8%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
0 2
 
0.5%
140 1
 
0.2%
180 1
 
0.2%
(Missing) 12
 
3.0%
ValueCountFrequency (%)
0 2
 
0.5%
50 5
 
1.2%
60 71
17.8%
70 111
27.8%
80 115
28.7%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 115
28.7%
70 111
27.8%
60 71
17.8%
50 5
 
1.2%

sg
Categorical

High correlation  Missing 

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size3.3 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4617564
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 106
26.5%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%
(Missing) 47
11.8%

Length

2025-06-24T12:08:58.095557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:08:58.368069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.02 106
30.0%
1.01 84
23.8%
1.025 81
22.9%
1.015 75
21.2%
1.005 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

al
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.0169492
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:08:58.624234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3526789
Coefficient of variation (CV)1.3301343
Kurtosis-0.3833766
Mean1.0169492
Median Absolute Deviation (MAD)0
Skewness0.99815724
Sum360
Variance1.8297402
MonotonicityNot monotonic
2025-06-24T12:08:58.900117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
(Missing) 46
 
11.5%
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 44
 
11.0%
0 199
49.8%

su
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.45014245
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:08:59.156865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0991913
Coefficient of variation (CV)2.4418742
Kurtosis5.055348
Mean0.45014245
Median Absolute Deviation (MAD)0
Skewness2.4642618
Sum158
Variance1.2082214
MonotonicityNot monotonic
2025-06-24T12:08:59.441335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
72.5%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
(Missing) 49
 
12.2%
ValueCountFrequency (%)
0 290
72.5%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 290
72.5%

rbc
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size3.3 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.3790323
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 201
50.2%
abnormal 47
 
11.8%
(Missing) 152
38.0%

Length

2025-06-24T12:08:59.811280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:00.121347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 201
81.0%
abnormal 47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

pc
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size3.3 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.4537313
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 259
64.8%
abnormal 76
 
19.0%
(Missing) 65
 
16.2%

Length

2025-06-24T12:09:00.416114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:00.732391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 259
77.3%
abnormal 76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

pcc
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.3 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.6818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 354
88.5%
present 42
 
10.5%
(Missing) 4
 
1.0%

Length

2025-06-24T12:09:01.039911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:01.333583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 354
89.4%
present 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

ba
Categorical

Imbalance 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.3 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.8333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 374
93.5%
present 22
 
5.5%
(Missing) 4
 
1.0%

Length

2025-06-24T12:09:01.671503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:02.036010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 374
94.4%
present 22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

bgr
Real number (ℝ)

High correlation  Missing 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:02.408158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2025-06-24T12:09:02.827630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

bu
Real number (ℝ)

High correlation  Missing 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:03.237042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2025-06-24T12:09:03.636318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

sc
Real number (ℝ)

High correlation  Missing 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:04.089301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2025-06-24T12:09:04.488875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sod
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.52875
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:04.864860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.408752
Coefficient of variation (CV)0.075684188
Kurtosis85.53437
Mean137.52875
Median Absolute Deviation (MAD)3
Skewness-6.9965686
Sum43046.5
Variance108.34212
MonotonicityNot monotonic
2025-06-24T12:09:05.229635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

pot
Real number (ℝ)

Missing 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.6272436
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:05.644881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.1939042
Coefficient of variation (CV)0.69023904
Kurtosis142.50591
Mean4.6272436
Median Absolute Deviation (MAD)0.5
Skewness11.582956
Sum1443.7
Variance10.201024
MonotonicityNot monotonic
2025-06-24T12:09:06.022707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
3.8 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

hemo
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.474713
Minimum0
Maximum17.8
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:06.458596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.77
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range17.8
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.0408162
Coefficient of variation (CV)0.24375842
Kurtosis0.68993626
Mean12.474713
Median Absolute Deviation (MAD)2.35
Skewness-0.61412248
Sum4341.2
Variance9.2465633
MonotonicityNot monotonic
2025-06-24T12:09:06.887298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
0 2
0.5%
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

pcv
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.26
Minimum9
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:07.305100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q134
median41
Q344
95-th percentile52
Maximum54
Range45
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1911622
Coefficient of variation (CV)0.20863887
Kurtosis0.29614118
Mean39.26
Median Absolute Deviation (MAD)5
Skewness-0.60605471
Sum15704
Variance67.095138
MonotonicityNot monotonic
2025-06-24T12:09:07.667152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41 92
23.0%
52 21
 
5.2%
44 19
 
4.8%
48 19
 
4.8%
40 16
 
4.0%
43 15
 
3.8%
45 13
 
3.2%
42 13
 
3.2%
33 12
 
3.0%
50 12
 
3.0%
Other values (32) 168
42.0%
ValueCountFrequency (%)
9 1
 
0.2%
14 1
 
0.2%
15 1
 
0.2%
16 1
 
0.2%
17 1
 
0.2%
18 1
 
0.2%
19 2
0.5%
20 1
 
0.2%
21 1
 
0.2%
22 3
0.8%
ValueCountFrequency (%)
54 4
 
1.0%
53 4
 
1.0%
52 21
5.2%
51 4
 
1.0%
50 12
3.0%
49 4
 
1.0%
48 19
4.8%
47 4
 
1.0%
46 9
2.2%
45 13
3.2%

wc
Real number (ℝ)

Distinct89
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8775.5
Minimum2200
Maximum26400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:07.951553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4700
Q16975
median9450
Q39800
95-th percentile12400
Maximum26400
Range24200
Interquartile range (IQR)2825

Descriptive statistics

Standard deviation2597.3091
Coefficient of variation (CV)0.29597278
Kurtosis7.1814226
Mean8775.5
Median Absolute Deviation (MAD)1250
Skewness1.3413901
Sum3510200
Variance6746014.8
MonotonicityNot monotonic
2025-06-24T12:09:08.332868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9800 117
29.2%
6700 10
 
2.5%
9200 9
 
2.2%
9600 9
 
2.2%
7200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
7000 7
 
1.8%
Other values (79) 208
52.0%
ValueCountFrequency (%)
2200 1
 
0.2%
2600 1
 
0.2%
3800 2
 
0.5%
4100 1
 
0.2%
4200 3
0.8%
4300 6
1.5%
4500 3
0.8%
4700 4
1.0%
4900 1
 
0.2%
5000 5
1.2%
ValueCountFrequency (%)
26400 1
0.2%
21600 1
0.2%
19100 1
0.2%
18900 1
0.2%
16700 1
0.2%
16300 1
0.2%
15700 1
0.2%
15200 2
0.5%
14900 1
0.2%
14600 2
0.5%

rc
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.86875
Minimum2.1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2025-06-24T12:09:08.681763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile3.2
Q14.5
median5.2
Q35.2
95-th percentile6.105
Maximum8
Range5.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.87160638
Coefficient of variation (CV)0.17902057
Kurtosis0.85745008
Mean4.86875
Median Absolute Deviation (MAD)0.35
Skewness-0.70482162
Sum1947.5
Variance0.75969768
MonotonicityNot monotonic
2025-06-24T12:09:09.098886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5.2 149
37.2%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
5 10
 
2.5%
3.9 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
5.9 8
 
2.0%
Other values (35) 154
38.5%
ValueCountFrequency (%)
2.1 2
0.5%
2.3 1
 
0.2%
2.4 1
 
0.2%
2.5 2
0.5%
2.6 2
0.5%
2.7 2
0.5%
2.8 2
0.5%
2.9 2
0.5%
3 3
0.8%
3.1 2
0.5%
ValueCountFrequency (%)
8 1
 
0.2%
6.5 5
1.2%
6.4 5
1.2%
6.3 4
1.0%
6.2 5
1.2%
6.1 8
2.0%
6 4
1.0%
5.9 8
2.0%
5.8 7
1.8%
5.7 5
1.2%

htn
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size932.0 B
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False 251
62.7%
True 147
36.8%
(Missing) 2
 
0.5%
2025-06-24T12:09:09.431931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

dm
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size932.0 B
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False 261
65.2%
True 137
34.2%
(Missing) 2
 
0.5%
2025-06-24T12:09:09.737591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

cad
Boolean

Imbalance 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size932.0 B
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False 364
91.0%
True 34
 
8.5%
(Missing) 2
 
0.5%
2025-06-24T12:09:09.988696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.3 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good 317
79.2%
poor 82
 
20.5%
(Missing) 1
 
0.2%

Length

2025-06-24T12:09:10.285537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:10.601536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
good 317
79.4%
poor 82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

pe
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size932.0 B
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False 323
80.8%
True 76
 
19.0%
(Missing) 1
 
0.2%
2025-06-24T12:09:10.851628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

ane
Boolean

High correlation 

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size932.0 B
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False 339
84.8%
True 60
 
15.0%
(Missing) 1
 
0.2%
2025-06-24T12:09:11.106970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

classification
Categorical

High correlation 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
ckd
250 
notckd
150 

Length

Max length6
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters1650
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowckd
2nd rowckd
3rd rowckd
4th rowckd
5th rowckd

Common Values

ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Length

2025-06-24T12:09:11.462204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T12:09:11.773410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Most occurring characters

ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 400
24.2%
k 400
24.2%
d 400
24.2%
n 150
 
9.1%
o 150
 
9.1%
t 150
 
9.1%

Interactions

2025-06-24T12:08:49.778467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:04.018671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:07.695841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:10.943317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:14.204370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:18.439929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:22.224054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:26.005925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:29.995994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:33.590599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:37.100052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:40.375657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:43.612895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:46.653134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:50.034282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:04.312298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:07.934118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:11.177529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:14.458125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:18.745985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:22.497149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:26.304785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:30.326482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:33.865264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:37.294004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:40.626787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:43.841977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:46.917818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:50.304163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:04.541609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:08.366956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:11.364533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:14.702985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:18.992985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:22.752986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:26.549668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:30.542028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:34.089181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:37.471969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:40.821461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:44.150187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:47.106297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:50.523022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:04.818440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:08.597793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:11.598236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:14.951010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:19.307973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:23.061666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:26.820245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:30.787109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:34.375313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:37.680788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:41.069833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:44.372312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:47.342563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:50.724308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:05.056074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:08.795709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:11.847666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:15.232466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:19.618554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:23.415419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:27.419805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:31.006627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:34.623150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:37.832372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:41.317283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:44.571772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:47.531882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:50.937109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:05.294601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:08.975130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:12.057144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:15.505070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:19.912201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:23.723857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:27.600442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:31.285410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:34.851822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:38.016496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:41.514599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:44.738476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:47.732332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:51.168763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:05.526989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:09.171350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:12.313824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:16.200989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:20.211404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:24.038732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:27.820895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:31.542971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:35.081183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:38.243196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:41.745963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:44.923987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:47.921793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:51.404469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:05.819056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:09.391355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:12.591058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:16.531780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:20.542016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:24.379989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:28.090192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:31.799306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:35.399207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:38.449060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:41.950825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:45.136336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:48.156954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:51.639829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:06.094727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:09.622014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:12.836363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:16.818022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:20.801856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:24.663031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:28.336974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:32.078552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:35.651395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:38.638748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:42.199999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:45.404960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:48.404915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:51.871880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-06-24T12:08:24.929462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:28.655731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:32.375158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:35.897669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:39.258625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:42.425991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:45.656188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:48.687666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:52.086328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:06.579453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:10.072522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:13.310520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:17.367105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:21.271015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:25.106139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:28.915451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:32.602829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:36.095282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:39.442109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:42.610169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:45.876860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:48.884883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:52.290197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:06.832027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:10.325286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:13.545954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:17.665147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:21.558483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:25.361343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:29.206315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:32.873573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:36.378370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:39.731166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:42.853428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:46.103943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:49.119755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:52.469837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:07.217321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:10.519087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:13.786519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:17.926082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:21.778994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:25.570877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:29.463385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:33.126017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:36.624344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:39.943128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:43.048643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:46.267679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:49.359257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:52.690403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:07.431193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:10.734129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:14.002928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:18.167226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:21.997772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:25.791117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:29.684437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:33.341846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:36.840876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:40.126240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:43.315618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:46.455721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-24T12:08:49.552885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-06-24T12:09:12.090164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
agealaneappetbabgrbpbucadclassificationdmhemohtnidpcpccpcvpepotrbcrcscsgsodsuwc
age1.0000.2130.1270.1290.0730.2990.1200.3090.2080.3490.367-0.2220.392-0.2170.1330.180-0.2680.1160.0720.064-0.2120.3500.088-0.1340.2810.166
al0.2131.0000.3440.3770.4100.3720.1870.4950.3320.7260.458-0.6840.549-0.6110.5880.453-0.6130.4740.0530.533-0.3980.6410.287-0.5340.3580.244
ane0.1270.3441.0000.2410.0000.1350.1310.4540.0000.3140.1670.7000.3360.2630.3150.1550.5780.1910.1680.1630.4160.3750.2490.3280.1450.214
appet0.1290.3770.2411.0000.1250.2500.1460.2650.1350.3830.3130.4230.3330.3970.3030.1710.3500.4060.0750.2620.3770.1410.2740.2400.2570.235
ba0.0730.4100.0000.1251.0000.1040.0000.2300.1330.1670.0430.1990.0560.1560.3110.2520.1480.1080.0000.1480.1430.0000.2040.1600.1860.166
bgr0.2990.3720.1350.2500.1041.0000.1830.1950.3060.4590.576-0.3560.446-0.3510.3870.176-0.3300.2070.0720.364-0.2320.3590.209-0.2610.6020.154
bp0.1200.1870.1310.1460.0000.1831.0000.1850.0980.2310.148-0.2670.245-0.2310.1190.000-0.2570.0630.0970.183-0.1960.2990.103-0.1330.2190.055
bu0.3090.4950.4540.2650.2300.1950.1851.0000.2780.3810.365-0.5860.471-0.3430.4080.207-0.5110.3180.2120.322-0.4000.7030.197-0.4140.2230.111
cad0.2080.3320.0000.1350.1330.3060.0980.2781.0000.2200.2560.2500.3120.1880.1940.1650.3000.1520.0000.1610.3250.1430.1580.2110.3820.000
classification0.3490.7260.3140.3830.1670.4590.2310.3810.2201.0000.5500.8180.5820.9490.4520.2500.6720.3650.0000.5420.6340.1850.7890.3960.3660.403
dm0.3670.4580.1670.3130.0430.5760.1480.3650.2560.5501.0000.5050.6000.5270.2890.1450.4520.2960.0160.3210.4320.1790.4500.3010.5490.157
hemo-0.222-0.6840.7000.4230.199-0.356-0.267-0.5860.2500.8180.5051.0000.5890.6860.5610.3680.8260.445-0.0640.4770.544-0.7210.3210.511-0.307-0.242
htn0.3920.5490.3360.3330.0560.4460.2450.4710.3120.5820.6000.5891.0000.5490.3720.1770.5340.3600.0590.2890.5410.1800.4190.3640.3700.133
id-0.217-0.6110.2630.3970.156-0.351-0.231-0.3430.1880.9490.5270.6860.5491.0000.4040.2390.6230.332-0.0300.5260.395-0.6010.3820.489-0.302-0.280
pc0.1330.5880.3150.3030.3110.3870.1190.4080.1940.4520.2890.5610.3720.4041.0000.5010.4610.4030.1850.4100.4390.2410.3850.3450.2220.227
pcc0.1800.4530.1550.1710.2520.1760.0000.2070.1650.2500.1450.3680.1770.2390.5011.0000.3280.0770.0000.0690.2430.0000.2840.2610.1970.226
pcv-0.268-0.6130.5780.3500.148-0.330-0.257-0.5110.3000.6720.4520.8260.5340.6230.4610.3281.0000.351-0.1230.4130.565-0.6650.2870.485-0.286-0.228
pe0.1160.4740.1910.4060.1080.2070.0630.3180.1520.3650.2960.4450.3600.3320.4030.0770.3511.0000.1350.2820.3190.2720.3520.2160.1650.201
pot0.0720.0530.1680.0750.0000.0720.0970.2120.0000.0000.016-0.0640.059-0.0300.1850.000-0.1230.1351.0000.000-0.0840.1290.0390.0210.055-0.071
rbc0.0640.5330.1630.2620.1480.3640.1830.3220.1610.5420.3210.4770.2890.5260.4100.0690.4130.2820.0001.0000.2980.2090.4350.2920.2130.261
rc-0.212-0.3980.4160.3770.143-0.232-0.196-0.4000.3250.6340.4320.5440.5410.3950.4390.2430.5650.319-0.0840.2981.000-0.4250.2450.306-0.207-0.072
sc0.3500.6410.3750.1410.0000.3590.2990.7030.1430.1850.179-0.7210.180-0.6010.2410.000-0.6650.2720.1290.209-0.4251.0000.139-0.4970.3560.209
sg0.0880.2870.2490.2740.2040.2090.1030.1970.1580.7890.4500.3210.4190.3820.3850.2840.2870.3520.0390.4350.2450.1391.0000.2320.1830.214
sod-0.134-0.5340.3280.2400.160-0.261-0.133-0.4140.2110.3960.3010.5110.3640.4890.3450.2610.4850.2160.0210.2920.306-0.4970.2321.000-0.229-0.043
su0.2810.3580.1450.2570.1860.6020.2190.2230.3820.3660.549-0.3070.370-0.3020.2220.197-0.2860.1650.0550.213-0.2070.3560.183-0.2291.0000.223
wc0.1660.2440.2140.2350.1660.1540.0550.1110.0000.4030.157-0.2420.133-0.2800.2270.226-0.2280.201-0.0710.261-0.0720.2090.214-0.0430.2231.000

Missing values

2025-06-24T12:08:53.618447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-24T12:08:54.510937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-24T12:08:55.345022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
00.048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.44478005.2yesyesnogoodnonockd
11.07.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.33860005.2nononogoodnonockd
22.062.080.01.0102.03.0normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.63175005.2noyesnopoornoyesckd
33.048.070.01.0054.00.0normalabnormalpresentnotpresent117.056.03.8111.02.511.23267003.9yesnonopooryesyesckd
44.051.00.01.0102.00.0normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.63573004.6nononogoodnonockd
55.060.090.01.0153.00.0NaNNaNnotpresentnotpresent74.025.01.1142.03.212.23978004.4yesyesnogoodyesnockd
66.068.070.01.0100.00.0NaNnormalnotpresentnotpresent100.054.024.0104.04.012.43698005.2nononogoodnonockd
77.024.0NaN1.0152.04.0normalabnormalnotpresentnotpresent410.031.01.1NaNNaN0.04469005.0noyesnogoodyesnockd
88.052.0100.01.0153.00.0normalabnormalpresentnotpresent138.060.01.9NaNNaN10.83396004.0yesyesnogoodnoyesckd
99.053.090.01.0202.00.0abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529121003.7yesyesnopoornoyesckd
idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
390390.052.080.01.0250.00.0normalnormalnotpresentnotpresent99.025.00.8135.03.715.05263005.3nononogoodnononotckd
391391.036.080.01.0250.00.0normalnormalnotpresentnotpresent85.016.01.1142.04.115.64458006.3nononogoodnononotckd
392392.057.080.01.0200.00.0normalnormalnotpresentnotpresent133.048.01.2147.04.314.84666005.5nononogoodnononotckd
393393.043.060.01.0250.00.0normalnormalnotpresentnotpresent117.045.00.7141.04.413.05474005.4nononogoodnononotckd
394394.050.080.01.0200.00.0normalnormalnotpresentnotpresent137.046.00.8139.05.014.14595004.6nononogoodnononotckd
395395.055.080.01.0200.00.0normalnormalnotpresentnotpresent140.049.00.5150.04.915.74767004.9nononogoodnononotckd
396396.042.070.01.0250.00.0normalnormalnotpresentnotpresent75.031.01.2141.03.516.55478006.2nononogoodnononotckd
397397.012.080.01.0200.00.0normalnormalnotpresentnotpresent100.026.00.6137.04.415.84966005.4nononogoodnononotckd
398398.017.060.01.0250.00.0normalnormalnotpresentnotpresent114.050.01.0135.04.914.25172005.9nononogoodnononotckd
399399.058.080.01.0250.00.0normalnormalnotpresentnotpresent131.018.01.1141.03.515.85368006.1nononogoodnononotckd